End-to-end AI engineering.
Not off-the-shelf wrappers.
Every engagement is built from scratch for your data, your constraints, and your scale targets. We cover the full ML lifecycle — from raw data to production serving.
Computer Vision
YOLO, SAM, EfficientDet — object detection, segmentation, OCR, defect analysis. Edge or cloud deployment.
NLP & Text AI
Sentiment analysis, entity extraction, classification, summarisation, semantic search — 20+ languages.
Data Engineering
Spark, dbt, Airflow pipelines. Feature stores, data lakes, real-time streaming. Clean data for clean models.
Models measured at every layer.
Not just on demo day.
Every model we ship includes a production monitoring setup — accuracy tracking, latency SLOs, data drift alerting, and automated retraining. You see the numbers. We guarantee them.
We pick the right model.
Not the most hyped one.
Cloud APIs, open-weights, fine-tuned — we evaluate all options against your latency budget, data sensitivity, and cost targets. The model that ships is the one that actually fits.
Knowledge-Grounded Answers, Not Hallucinations
Retrieval-Augmented Generation gives your LLM a live, searchable brain. We architect the full pipeline: ingestion, chunking strategy, embedding model selection, vector store, hybrid search, reranking, and prompt construction.
- Hybrid BM25 + dense vector retrieval
- Cross-encoder reranking for precision
- Pinecone, Weaviate, pgvector, or Qdrant
- Source citations and confidence scores
- Automated evaluation with RAGAS
- Streaming responses via FastAPI
from langchain.vectorstores import Pinecone
from langchain.retrievers import ContextualCompressionRetriever
retriever = vectorstore.as_retriever(
search_type="mmr",
search_kwargs={"k": 8, "fetch_k": 20}
)
compressor = CohereRerank(top_n=4)
pipeline = ContextualCompressionRetriever(
base_compressor=compressor,
base_retriever=retriever
)
# Returns cited, grounded answers
answer = qa_chain.invoke({"query": user_input})From Data to Production in Weeks
We assess your data maturity, quality, and labelling gaps. Define success metrics, baselines, and the ML framing (classification vs generation vs ranking).
Architecture selection, feature engineering, hyperparameter search. We run experiment tracking in MLflow and deliver reproducible training pipelines.
Held-out test sets, cross-validation, bias audits, adversarial probing. Every model ships with an eval report and a documented failure-mode catalogue.
Containerised serving on Kubernetes, auto-scaling, canary rollouts. Drift alerts, retraining triggers, and a Grafana dashboard included.
Ready to ship AI that
actually works in production?
Share your use case. We'll scope it, price it fixed, and deliver it — with documented accuracy targets, IP protection, and a handoff that your engineering team can own.